“Tracking Character Diversity in the Animation Pipeline” by Kanyuk, MacMahon, Wilson, Nye, Cameron, et al. …

  • ©Paul Kanyuk, Mara MacMahon, Emily Wilson, Peter Nye, Gordon Cameron, Jessica Heidt, and Joshua Minor

Conference:


Type:


Entry Number: 52

Title:

    Tracking Character Diversity in the Animation Pipeline

Presenter(s)/Author(s):



Abstract:


    As we explore a broad range of characters and stories in our films, it has become increasingly valuable to view breakdowns of our character pools and selections by demographic: to build and use our assets efficiently, reinforce storytelling and world building choices, and ensure consistent decision-making across the pipeline. With the Character Linker App within Traction (Traction is Pixar’s asset and shot-tracking tool), production is able to see a live breakdown of the character pool as assets are built, and sequence/shot composition, as they are populated–with the ability to visualize by a range of categories, including gender, ethnicity, body-type, and age, among others. Each film can define and populate these categories specific to their story, set breakdown goals to measure progress against, and iterate on crowd asset selections to ensure each character is utilized to the fullest.

References:


    Erika Doggett, Anna Maria Wolak, P. Daphne Tsatsoulis, and Nicholas McCarthy. 2019. Neural pixel error detection. ACM SIGGRAPH 2019 Talks(2019).Google ScholarDigital Library
    Theodore Kim, Holly Rushmeier, Julie Dorsey, Derek Nowrouzezahrai, Raqi Syed, Wojciech Jarosz, and A. M. Darke. 2021. Countering Racial Bias in Computer Graphics Research. arxiv:2103.15163 [cs.GR]Google Scholar
    OpenTimelineIO. [n.d.]. Project Website: https://opentimeline.io/.Google Scholar
    Title = Project Website: https://openusd.org Universal Scene Description.[n.d.].Google Scholar


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